Optical fiber sensing method and device based on parameterized waveform buffering and modular coupling

Abstract

The invention discloses an optical fiber sensing method and device based on parameterized waveform buffering and modular coupling, and relates to the technical field of optical fiber sensing. Comprising the steps of initial data acquisition, external data monitoring, data preprocessing, data waveform buffering, modular data coupling, integration of building data and environment parameter data, deep analysis and mining, and reduction of data errors and inconsistency; abnormal data adjustment: detecting data abnormity, identifying an abnormal value in the data, and deleting, replacing or correcting the abnormal value; correction algorithm selection: monitoring and selecting a proper correction algorithm in real time based on the characteristics of data change, effectively coping with the data change, and improving the correction efficiency and accuracy; correcting data; and alarming and responding. According to the method, the waveform buffering effect and the accuracy of monitoring the accurate data are improved, the waveform buffering stability is further improved, meanwhile, data changes caused by the external environment can be effectively coped with, and the correction efficiency and accuracy are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of fiber optic sensing, and specifically to a fiber optic sensing method and device based on parametric waveform buffering and modular coupling. Background Art

[0002] Parametric waveform buffering is a signal processing technology that plays an important role in fiber optic sensing systems. Fiber optic sensing systems sense external signals through optical fibers and convert these signals into changes in optical wave parameters, such as intensity, wavelength, frequency, phase, and polarization state. Parametric waveform buffering is a key link for buffering and processing these changes in optical wave parameters. In civil engineering fiber optic sensing, parametric waveform buffering can be used to improve the stability and accuracy of the sensing system. By parametric waveform buffering, the received optical signals can be preprocessed to reduce noise and interference, thereby extracting more accurate structural state information, which is particularly suitable for real-time monitoring of large structures such as bridges, tunnels, and buildings.

[0003] A fiber optic sensing method, a fiber optic sensing device, and a method for using the same with the application number 201210039186.2 obtain the transmission function curve of a fiber grating by using a method in which an optical single sideband swept signal modulated by a microwave swept signal passes through the fiber grating. The fiber optic sensing device of the present invention includes: a microwave swept source, a narrow linewidth laser, a broadband optical single sideband modulator, a narrowband fiber grating sensor, a photodetector, a microwave amplitude and phase information extraction module, a data processing and control module, and a display module. The present invention also discloses a method for using the above fiber optic sensing device. Compared with the prior art, the present invention has high sensing accuracy and a large measurement range, and can achieve distributed sensing by cascading multiple narrowband fiber gratings with different stopband center wavelengths.

[0004] In the existing civil engineering fiber optic sensing method based on parametric waveform buffering and modular coupling, when collecting data, the parametric waveform buffering is easily affected by external factors, thereby affecting the effect of waveform buffering and the accuracy of monitoring data, and further affecting the stability of waveform buffering. The data is prone to change in different environments, and corresponding calibration algorithms need to be selected according to the data changes to improve the efficiency and accuracy of calibration. Summary of the Invention

[0005] The purpose of the present invention is to provide a fiber optic sensing method and device based on parametric waveform buffering and modular coupling to solve the problems raised in the above background art.

[0006] To achieve the above purpose, the present invention provides the following technical solution: A fiber optic sensing method based on parametric waveform buffering and modular coupling, the method includes:

[0007] Initial data collection, preset sampling frequency, and collect building data; external data monitoring, monitor environmental parameters, and obtain environmental parameter data;

[0008] Data preprocessing, preprocess the building data and environmental parameter data;

[0009] Data waveform buffering, perform waveform smoothing on the preprocessed data, establish a data buffer queue, and during the data waveform buffering process, monitor the changes in the data in real time;

[0010] Modular data coupling, integrate the building data and environmental parameter data, conduct in-depth analysis and mining, and reduce data errors and inconsistencies;

[0011] Abnormal data adjustment, detect data anomalies, identify the abnormal values in the data, and delete, replace, or correct the abnormal values;

[0012] Calibration algorithm selection, based on the characteristics of data changes, monitor and select an appropriate calibration algorithm in real time, effectively respond to data changes, and improve the efficiency and accuracy of calibration;

[0013] Data calibration, compare the features with the historical database, find the differences and deviations, and calibrate the data to correct the deviations and errors;

[0014] Alarm and response, compare the data with the preset threshold, determine whether the valid data exceeds the threshold, if it exceeds the limit, issue an alarm, and perform processing and feedback.

[0015] The waveform smoothing process for the preprocessed data is as follows:

[0016]

[0017] where y i is the i-th data point after smoothing, w is the weight of the smoothing process, representing the contribution of each original data point to the smoothed data point, and ∑ i j=max(1,i=w+1) x j is the sum of the original data points within the window, and max(1, i = w + 1) ensures that the starting point of the window is not less than 1.

[0018] The establishment of the data buffer queue;

[0019] where Q is the data buffer queue, storing the k most recent smoothed data points, y n = k + 1, y n = k + 2,..., y n = k + n are the smoothed data points in the queue;

[0020] During the data waveform buffering process, the changes in the data are monitored in real time, and the calculation formula is as follows:

[0021]

[0022]

[0023] Among them, Δμ is the mean difference of the data changes monitored in real time, μQ is the mean of the current data buffer queue Q, and μQ prev is the mean of the data buffer queue at the previous moment. k1 is the divisor when calculating the mean to ensure that the mean is the average of all data points in the queue, and ∑ n i=n-k+1 y i is the sum of all smoothed data points in the data buffer queue Q.

[0024] For the modular data coupling, the analysis formula is as follows:

[0025]

[0026] Among them, A represents the final analysis result, which is the result of in-depth analysis and mining after integrating building data and environmental parameter data. f represents an analysis function used to combine the weighted building data, environmental parameter data, and the consistency correction factor to obtain the final analysis result. ∑ n i=1 and ∑ m j=1 represent the summation operations, which are used to perform weighted summation on the building data and environmental parameter data respectively. i and j represent the indices of the building data and environmental parameter data respectively, and n and m represent the quantities of the building data and environmental parameter data respectively. B i represents the i-th building data item, W i represents the weight of the i-th building data item, which is used to reflect the importance of this data item in the analysis. E j represents the j-th environmental parameter data item, which can be specific environmental parameters such as temperature, humidity, noise, air quality, etc. W j represents the weight of the j-th environmental parameter data item, which is used to reflect the importance of this parameter in the analysis. C represents the consistency correction factor, which is used to correct possible errors and inconsistencies between the building data and environmental parameter data.

[0027] For the abnormal data adjustment, the adjustment formula is as follows:

[0028]

[0029] Among them, A i is the i-th data point after adjustment, O i is the original i-th data point, Fi is an outlier flag, which is 1 if it is an outlier and 0 otherwise, D i is a deletion factor. If it is determined to delete the outlier, it is 1; otherwise, it is 0, R i is a replacement factor. If it is determined to replace the outlier, it is 1; otherwise, it is 0, V is the new value used to replace the outlier, C i is a correction factor. If it is determined to correct the outlier, it is 1; otherwise, it is 0, K is the reference value of the corrected data point, σ is the standard deviation of the data set, ϵ is a very small positive number used to avoid a zero denominator, Z i is the standardized value of the i-th data point, MAD is the median absolute deviation of the data set, which is used to measure the dispersion of the data, α, β, γ, ω are various exponents used to adjust the influence degree of different parts, and λ is the coefficient in the correction term.

[0030] The correction algorithm selection is based on the characteristics of data changes to monitor in real time and select an appropriate correction algorithm. The skewness formula is as follows:

[0031]

[0032] where n is the sample size of the data, x i the i-th data point, x̄ is the mean of the data, σ is the standard deviation of the data, (σx i -x̄) 3 is the cube of each data point after standardization, which is used to measure the degree of deviation of the data point from the mean;

[0033] The kurtosis formula is as follows:

[0034]

[0035] where n is the sample size of the data, x i the i-th data point, x̄ is the mean of the data, σ is the standard deviation of the data, [(x i -x̄) / σ] 4 is the fourth power of each data point after standardization, which is used to measure the sharpness of the data point relative to the mean, (n - 2)(n - 3)3(n - 1) 2 is a constant term used to adjust the kurtosis value to compare it with the kurtosis of the normal distribution (the kurtosis value is 3);

[0036] If the skewness is close to 0 and the kurtosis is close to 3, the data is close to a normal distribution, and correction algorithm one is selected. If the skewness or kurtosis deviates significantly from the normal value, the data may have a skewed or leptokurtic distribution, and correction algorithm two is selected. Based on the selected correction algorithm, the data is corrected to improve the accuracy and reliability of the data.

[0037] The formula for correcting the data is as follows:

[0038]

[0039] Among them, d i is the i-th element in the current dataset D, and d i ′ is the i-th element in the corrected dataset D′, and Δi is the combined correction term for the i-th element;

[0040] The calculation formula for the combined correction term Δi is as follows:

[0041]

[0042] Among them, α is the weight coefficient of the mean difference, β is the weight coefficient of the variance ratio difference, μD is the mean of the current dataset D, μH is the mean of the historical database H, and σ 2 D is the variance of the current dataset D, and σ 2 H is the variance of the historical database H, and D and H respectively represent the number of elements in the current dataset and the historical database.

[0043] For the said alarm and response, the real-time collected data is compared with a preset threshold to determine whether the data exceeds the limit. If the data exceeds the threshold, the system issues a warning message and feeds back the alarm information to relevant personnel to ensure that the problem is processed in a timely manner and the safe and stable operation of the system is guaranteed.

[0044] Compared with the prior art, the beneficial effects of the present invention are:

[0045] The fiber optic sensing method and device based on parametric waveform buffering and modular coupling can, through data waveform buffering, modular data coupling, and abnormal data adjustment, monitor the change of data in real time, integrate building data and environmental parameter data, reduce data errors and inconsistencies. At the same time, identify outliers in the data, delete, replace or correct the detected data anomalies, thereby improving the effect of waveform buffering and the accuracy of monitored data, and further improving the stability of waveform buffering.

[0046] Through the selection of correction algorithms, when the data is prone to change in different environments, based on the characteristics of data change, an appropriate correction algorithm is selected to effectively respond to data change and improve the efficiency and accuracy of correction. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 is the schematic diagram of the principle structure of the present invention;

[0048] Figure 2 is the schematic diagram of the principle structure of the parametric waveform buffering of the present invention;

[0049] Figure 3Schematic diagram of the principle structure for the algorithm selection of the present invention;

[0050] Figure 4 Schematic diagram of the principle structure for the data correction in the present invention;

[0051] Figure 5 Schematic diagram of the principle structure for the threshold warning of the present invention. Detailed implementation manners

[0052] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0053] In this application, for the convenience of understanding, the method steps used do not necessarily need to be executed in the order of the steps in this embodiment during actual operation. In some other embodiments, these steps can be performed synchronously or the order can be changed.

[0054] As Figures 1 - 5 shown, the present invention provides a technical solution: a fiber optic sensing method based on parametric waveform buffering and modular coupling, and the method includes:

[0055] Initial data acquisition, presetting a sampling frequency, and collecting building data; external data monitoring, monitoring environmental parameters to obtain environmental parameter data;

[0056] Data preprocessing, preprocessing the building data and environmental parameter data;

[0057] Data waveform buffering, performing waveform smoothing processing on the preprocessed data, establishing a data buffer queue, and during the data waveform buffering process, monitoring the change situation of the data in real time;

[0058] Modular data coupling, integrating the building data and environmental parameter data, and performing in-depth analysis and mining to reduce data errors and inconsistencies;

[0059] Abnormal data adjustment, detecting data anomalies, identifying the abnormal values in the data, and deleting, replacing or correcting the abnormal values;

[0060] Correction algorithm selection, based on the characteristics of data changes, monitoring in real time and selecting an appropriate correction algorithm to effectively respond to data changes and improve the efficiency and accuracy of correction;

[0061] Data correction, comparing the features with the historical database to find the differences and deviations, and correcting the data to correct the deviations and errors;

[0062] Alarm and response: Compare the data with a preset threshold to determine whether the valid data exceeds the threshold. If it exceeds the limit, an alarm is issued, and processing and feedback are carried out.

[0063] The preprocessed data is subjected to waveform smoothing. The smoothing formula is as follows:

[0064]

[0065] Where, y i is the i-th data point after smoothing, w is the weight of the smoothing process, representing the contribution of each original data point to the smoothed data point, and ∑ i j=max(1,i=w+1) x j is the sum of the original data points within the window. max(1, i = w + 1) ensures that the starting point of the window is not less than 1.

[0066] It should be noted that when substituting data for calculation:

[0067] x = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100],

[0068] Set the window size w = 3,

[0069] We will gradually calculate each smoothed data point y i，

[0070] Calculate y1:

[0071] The window is

[10] (because there is only one data point),

[0072] y1 = 10 / 1,

[0073] = 10,

[0074] Calculate y2. The window is [10, 20],

[0075] y2 = (10 + 20) / 2,

[0076] = 15,

[0077] Calculate y3. The window is [10, 20, 30],

[0078] y3 = (10 + 20 + 30) / 3,

[0079] = 20,

[0080] Calculate y4. The window is [20, 30, 40],

[0081] y4 = (20 + 30 + 40) / 3,

[0082] = 30,

[0083] Calculate y5, with the window being [30, 40, 50],

[0084] y5 = (30 + 40 + 50) / 3,

[0085] = 40,

[0086] Calculate y6, with the window being [40, 50, 60],

[0087] y6 = (40 + 50 + 60) / 3,

[0088] = 50,

[0089] Calculate y7, with the window being [50, 60, 70],

[0090] y7 = (50 + 60 + 70) / 3,

[0091] = 60,

[0092] Calculate y8, with the window being [60, 70, 80],

[0093] y8 = (60 + 70 + 80) / 3,

[0094] = 70,

[0095] Calculate y9, with the window being [70, 80, 90],

[0096] y9 = (70 + 80 + 90) / 3,

[0097] = 80,

[0098] Calculate y10, with the window being [80, 90, 100],

[0099] y10 = (80 + 90 + 100) / 3,

[0100] = 90,

[0101] Therefore, the smoothed data sequence is:

[0102] y = [10, 15, 20, 30, 40, 50, 60, 70, 80, 90].

[0103] Establish the data buffer queue;

[0104] Among them, Q is the data buffer queue, storing the most recent k smoothed data points, y n = k + 1, y n = k + 2,..., y n = k + n are the smoothed data points in the queue;

[0105] During the data waveform buffering process, the changes in the data are monitored in real time, and the calculation formula is as follows:

[0106]

[0107]

[0108] Among them, Δμ is the mean difference of the data changes monitored in real time, μQ is the mean of the current data buffer queue Q, and μQ prev is the mean of the data buffer queue at the previous moment. k1 is the divisor when calculating the mean to ensure that the mean is the average of all data points in the queue. ∑ n i=n-k+1 y i is the sum of all smoothed data points in the data buffer queue Q;

[0109] It should be noted that when substituting data for calculation:

[0110] Establish a data buffer queue,

[0111] The smoothed data sequence y = [y1, y2, …, yn], and a buffer queue Q containing the last k smoothed data points is established.

[0112] Given the smoothed data sequence y = [10, 15, 20, 30, 40, 50, 60, 70, 80, 90] (obtained from previous calculations), and we set k = 5, then the initial data buffer queue Q is:

[0113] Q = [10, 15, 20, 30, 40],

[0114] Calculate the mean μQ of the current data buffer queue,

[0115] For the initial queue Q = [10, 15, 20, 30, 40], we have:

[0116] μQ = (10 + 15 + 20 + 30 + 40) / 5,

[0117] = 23,

[0118] Monitor the data changes in real time and calculate the mean difference Δμ,

[0119] As time goes by, we obtain new smoothed data points and need to update the data buffer queue. At the same time, we calculate the mean difference Δμ between the current queue and the queue at the previous moment.

[0120] Among them, μQ prev is the mean of the data buffer queue at the previous moment,

[0121] Example calculation:

[0122] Initial moment:

[0123] Q = [10, 15, 20, 30, 40],

[0124] μQ = 23,

[0125] μQ prev does not exist (or set to 0),

[0126] Δμ = 23 - 0,

[0127] = 23,

[0128] Next moment (preset new data point as 50, queue updated to [15, 20, 30, 40, 50]):

[0129] μQ prev = 23,

[0130] μQ = (15 + 20 + 30 + 40 + 50) / 5,

[0131] = 31,

[0132] Δμ = 31 - 23,

[0133] = 8,

[0134] Continuing this process, we can monitor the changes in data in real time.

[0135] The modular data coupling analysis formula is as follows:

[0136]

[0137] Among them, A represents the final analysis result, which is the result of in-depth analysis and mining after integrating building data and environmental parameter data. f represents an analysis function used to combine the weighted building data, environmental parameter data, and consistency correction factor to obtain the final analysis result. ∑ n i=1 and ∑ m j=1 represent summation operations, which are used to perform weighted summation on building data and environmental parameter data respectively. i and j represent the indices of building data and environmental parameter data respectively. n and m represent the quantities of building data and environmental parameter data respectively. B i represents the i-th building data item. W i represents the weight of the i-th building data item, which is used to reflect the importance of this data item in the analysis. E j represents the j-th environmental parameter data item, which can be specific environmental parameters such as temperature, humidity, noise, air quality, etc. W jrepresents the weight of the j-th environmental parameter data item, which is used to reflect the importance of this parameter in the analysis. C represents the consistency correction factor, which is used to correct possible errors and inconsistencies between the building data and the environmental parameter data;

[0138] It should be noted that when substituting data for calculation:

[0139] According to the modular data coupling analysis formula and substituting the simulation data for calculation, we can proceed as follows:

[0140] Building data item Bi:

[0141] B1 = 10, B2 = 20, B3 = 30,

[0142] Environmental parameter data item Ej:

[0143] E1 = 5, E2 = 10, E3 = 15,

[0144] Weight Wi of the building data item:

[0145] W1 = 0.2, W2 = 0.3, W3 = 0.5,

[0146] Weight Wj of the environmental parameter data item:

[0147] W1 = 0.4, W2 = 0.3, W3 = 0.3,

[0148] Consistency correction factor C:

[0149] C = 0.8,

[0150] Calculate the weighted sum,

[0151] Weighted sum of the building data item:

[0152] ∑ 3 i=1 ×B i ×W i = 10×0.2 + 20×0.3 + 30×0.5,

[0153] = 2 + 6 + 15,

[0154] = 23,

[0155] Weighted sum of the environmental parameter data item:

[0156] ∑ 3 j=1 ×E j ×W j = 5×0.4 + 10×0.3 + 15×0.3,

[0157] = 2 + 3 + 4.5,

[0158] = 9.5,

[0159] Substitute the weighted sum result and the consistency correction factor into the analysis function:

[0160] A = f(23, 9.5, 0.8) = 0.5×23 + 0.5×9.5 + 0.5×0.8,

[0161] = 11.5 + 4.75 + 0.4,

[0162] = 16.65,

[0163] Therefore, by substituting data for calculation, the final analysis result A is 16.65. This result is obtained through the modular data coupling analysis formula based on the given building data, environmental parameter data, weights, and the consistency correction factor.

[0164] The abnormal data adjustment is as follows:

[0165]

[0166] Where A i is the adjusted i-th data point, O i is the original i-th data point, F i is the outlier flag, which is 1 if it is an outlier, otherwise 0, D i is the deletion factor, which is 1 if it is determined to delete the outlier, otherwise 0, R i is the replacement factor, which is 1 if it is determined to replace the outlier, otherwise 0, V is the new value used to replace the outlier, C i is the correction factor, which is 1 if it is determined to correct the outlier, otherwise 0, K is the reference value of the corrected data point, σ is the standard deviation of the data set, ϵ is a very small positive number used to avoid a zero denominator, Z i is the standardized value of the i-th data point, MAD is the median absolute deviation of the data set, which is used to measure the dispersion of the data, α, β, γ, ω are various exponents used to adjust the influence degree of different parts, and λ is the coefficient in the correction term;

[0167] It should be noted that when substituting data for calculation:

[0168] The calculation process of the abnormal data adjustment formula is as follows:

[0169] Determine the outlier:

[0170] Given Fi = 1, it means that Oi = 10 is an outlier,

[0171] Calculate the standardized value:

[0172] The standardized value Zi is given as 1, where we assume it has already been calculated based on the dataset mean μ and standard deviation σ.

[0173] Calculate the median absolute deviation:

[0174] MAD = 1.5 is given, representing the median absolute deviation of the dataset.

[0175] Substitute into the formula to calculate the adjusted data points:

[0176] O i = 10,

[0177] F i = 1,

[0178] D i = 0,

[0179] R i = 1,

[0180] V = 5,

[0181] C i = 1,

[0182] K = 8,

[0183] σ = 2,

[0184] ϵ = 0.01,

[0185] Z i = 1,

[0186] MAD = 1.5,

[0187] α = 1,

[0188] β = 2,

[0189] γ = 3,

[0190] λ = 0.5,

[0191] ω = 2,

[0192] Calculate each term in the step-by-step formula:

[0193] Calculate ∣O i - V∣,

[0194] = ∣10 - 5∣,

[0195] = 5,

[0196] Calculate σ + ϵ∣O i - V∣α,

[0197] = 5 / (2 + 0.01)

[0198] ≈2.4876,

[0199] Calculate ∣Z i ∣ β ,

[0200] =∣1∣ 2 ,

[0201] =1,

[0202] Calculate (1 + ∣Z i ∣ / MAD) γ ,

[0203] =(1 + 1 / 1.5) 3 ,

[0204] ≈4.445,

[0205] Calculate C i ×∣i∣ β ×(1 + ∣Z i ∣ / MAD) γ ,

[0206] =1×1×4.445,

[0207] =4.445,

[0208] The complete calculation is:

[0209] A d =10×[1 - 6.9326 / 10 + 13.5 / 10],

[0210] =10×[1 - 0.69326 + 1.35],

[0211] =10×0.65674,

[0212] =6.5674,

[0213] In summary, the adjusted data point A d is approximately 6.5674.

[0214] The described calibration algorithm selection monitors and selects an appropriate calibration algorithm in real time based on the characteristics of data changes. The skewness formula is as follows:

[0215]

[0216] where n is the sample size of the data, x i is the i-th data point, x̄ is the mean of the data, σ is the standard deviation of the data, and (σx i - x̄) 3 is the cube of each standardized data point, used to measure the degree of deviation of the data point from the mean;

[0217] Input data:

[0218] To calculate the skewness, we will use the given simulated data [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]. The following are the detailed calculation steps and results:

[0219] Calculate the mean $\bar{x}$:

[0220] $\bar{x}=(1 + 2 + 3 + 4 + 5 + 6 + 7 + 8 + 9 + 10) / 10$,

[0221] $=55 / 10$,

[0222] $=5.5$,

[0223] Calculate the standard deviation $\sigma$:

[0224] $\sigma=\sqrt{(82.5 / 10)}$,

[0225] $=\sqrt{8.25}$,

[0226] $\approx2.8723$,

[0227] Calculate the skewness $S$ k :

[0228] $S$ k $=10 / [(10 - 1)\times(10 - 2)]\sum$ 10 i=1 [($x$ i $-5.5) / 2.8723]$ 3 ,

[0229] $S$ k $=10 / (9\times8)\sum$ 10 i=1 [($x$ i $-5.5) / 2.8723]$ 3 ,

[0230] $S$ k $=10 / 72\sum$ 10 i=1 [($x$ i $-5.5) / 2.8723]$ 3 ,

[0231] Next, calculate the standardized cubic value for each data point:

[0232] [(1 - 5.5) / 2.8723] 3 $\approx - 2.373$,

[0233] [(2 - 5.5) / 2.8723] 3 $\approx - 1.384$,

[0234] [(3 - 5.5) / 2.8723] 3 ≈ -0.517,

[0235] [(4 - 5.5) / 2.8723] 3 ≈ -0.123,

[0236] [(5 - 5.5) / 2.8723] 3 ≈ -0.003,

[0237] [(6 - 5.5) / 2.8723] 3 ≈ 0.003,

[0238] [(7 - 5.5) / 2.8723] 3 ≈ 0.123,

[0239] [(8 - 5.5) / 2.8723] 3 ≈ 0.517,

[0240] [(9 - 5.5) / 2.8723] 3 ≈ 1.384,

[0241] [(10 - 5.5) / 2.8723] 3 ≈ 2.373,

[0242] Sum these values:

[0243] ∑ 10 i=1 [(xi - 5.5) / 2.8723] 3, ,

[0244] ≈ -2.373 - 1.384 - 0.517 - 0.123 - 0.003 + 0.003 + 0.123 + 0.517 + 1.384 + 2.373,

[0245] = 0,

[0246] Therefore, the skewness S k is:

[0247] S k = 10 / 72×0,

[0248] = 0,

[0249] Conclusion:

[0250] The skewness value Sk is 0, indicating that the data distribution is symmetric. In this case, a special correction algorithm may not be required because the data is already relatively evenly distributed on both sides of the mean.

[0251] The kurtosis formula is as follows:

[0252]

[0253] where n is the sample size of the data, x i is the i-th data point, x̄ is the mean of the data, σ is the standard deviation of the data, [(x i - x̄) / σ] 4 is the fourth power of each standardized data point, used to measure the sharpness of the data point relative to the mean, (n - 2)(n - 3)3(n - 1) 2 is a constant term used to adjust the kurtosis value to compare it with the kurtosis of the normal distribution (kurtosis value is 3);

[0254] If the skewness is close to 0 and the kurtosis is close to 3, the data is close to a normal distribution, and correction algorithm one is selected. If the skewness or kurtosis deviates significantly from the normal values, the data may exhibit skewness or leptokurtosis, and correction algorithm two is selected. Based on the selected correction algorithm, the data is corrected to improve the accuracy and reliability of the data.

[0255] Substitute the data as follows:

[0256] x1 = 4,

[0257] x2 = 5,

[0258] x3 = 6,

[0259] x4 = 7,

[0260] x5 = 8,

[0261] Calculate the mean x̄,

[0262] x̄ = (4 + 5 + 6 + 7 + 8) / 5,

[0263] = 6,

[0264] First, calculate the variance s 2 :

[0265] s 2 = 1 / 5×[(4 - 6) 2 + (5 - 6) 2 + (6 - 6) 2 + (7 - 6) 2 + (8 - 6) 2 ,

[0266] = 1 / 5×[4 + 1 + 0 + 1 + 4],

[0267] = 2,

[0268] Then calculate the standard deviation σ:

[0269] σ = √2,

[0270] Calculate the fourth power of each standardized data point,

[0271] [(4 - 6) / √2] 4 = 4,

[0272] [(5 - 6) / √2] 4 = 1 / 4,

[0273] [(6 - 6) / √2] 4 = 0,

[0274] [(7 - 6) / √2] 4 = 1 / 4,

[0275] [(8 - 6) / √2] 4 = 4,

[0276] Calculate the kurtosis by substituting the above values into the kurtosis formula: (5×6) / [(5 - 1)(5 - 2)(5 - 3)]×(4 + 1 / 4 + 0 + 1 / 4 + 4)-3×[(5 - 1)2 / (5 - 2)(5 - 3)],

[0277] = 30 / (4×3×2)×(8.5)-(3×16) / (3×2),

[0278] = 30 / 24×8.5 - 8,

[0279] = 10.625 - 8,

[0280] = 2.625,

[0281] Therefore, the kurtosis value of this set of simulated data is 2.625.

[0282] Perform the correction on the data, and the correction formula is as follows:

[0283]

[0284] where d i is the i-th element in the current data set D, d i ′ is the i-th element in the corrected data set D′, and Δi is the combined correction term for the i-th element;

[0285] To perform the correction on the data, we first need to clarify the correction formula and the calculation method of the combined correction term Δi. However, you have not given the specific correction formula and the detailed calculation formula of the combined correction term Δi. But I can provide a general framework and assume a possible correction formula and the calculation method of the combined correction term for demonstration.

[0286] Substitute a set of data for calculation,

[0287] D = {1, 2, 3, 4, 5},

[0288] Give the value of the combined correction term Δi,

[0289] To simplify the calculation, the values of the combined correction term Δi are respectively:

[0290] Δ = {0.1, 0.2, 0.3, 0.4, 0.5},

[0291] Use the formula d i ′ = d i + Δi to correct the data,

[0292] For d1 = 1, Δ1 = 0.1, then:

[0293] d1′ = 1 + 0.1,

[0294] = 1.1,

[0295] For d2 = 2, Δ2 = 0.2, then:

[0296] d2′ = 2 + 0.2,

[0297] = 2.2,

[0298] For d3 = 3, Δ3 = 0.3, then:

[0299] d3′ = 3 + 0.3,

[0300] = 3.3,

[0301] For d4 = 4, Δ4 = 0.4, then:

[0302] d4′ = 4 + 0.4,

[0303] = 4.4,

[0304] For d5 = 5, Δ5 = 0.5, then:

[0305] d5′ = 5 + 0.5,

[0306] = 5.5,

[0307] Obtain the corrected data set,

[0308] The corrected data set D′ is:

[0309] D′ = {1.1, 2.2, 3.3, 4.4, 5.5};

[0310] Through the above steps, we accurately corrected the data and obtained the corrected data set.

[0311] The calculation formula for the combined correction term Δi is as follows:

[0312]

[0313] Among them, α is the weight coefficient of the mean difference, β is the weight coefficient of the variance ratio difference, μD is the mean of the current dataset D, μH is the mean of the historical database H, σ 2 D is the variance of the current dataset D, σ 2 H is the variance of the historical database H, and D and H represent the number of elements in the current dataset and the historical database respectively;

[0314] The calculation formula of the combined correction term Δi involves multiple parameters, including the weight coefficient α of the mean difference, the weight coefficient β of the variance ratio difference, the mean μD of the current dataset D, the mean μH of the historical database H, the variance σ²D of the current dataset D, the variance σ²H of the historical database H, and the number of elements D in the current dataset D and the number of elements H in the historical database H (although the numbers of D and H are not directly used in the formula, understanding this background information helps to comprehensively grasp the problem). Now, we will calculate according to the provided simulation data.

[0315] Substitute the data:

[0316] α = 0.5,

[0317] β = 0.5,

[0318] μD = 10,

[0319] μH = 12,

[0320] σ²D = 4,

[0321] σ²H = 9,

[0322] d i (a certain element value in the current dataset D) = 9

[0323] Calculate according to the formula steps:

[0324] Calculate the mean difference part:

[0325] α×(μH - μD),

[0326] = 0.5×(12 - 10),

[0327] = 0.5×2,

[0328] = 1,

[0329] Calculate the variance ratio difference part:

[0330] √σ 2 H / σ2 D ,

[0331] =√9 / 4,

[0332] =√2.25,

[0333] =1.5,

[0334] [(√σ 2 H / σ 2 D )-1]×(d i -μD),

[0335] =(1.5-1)×(9-10),

[0336] =0.5×(-1),

[0337] =-0.5,

[0338] β×[(√σ 2 H / σ 2 D )-1]×(di-μD),

[0339] =0.5×(-0.5),

[0340] =-0.25,

[0341] Add the two parts together to get the value of Δi:

[0342] Δi = 1 + (-0.25),

[0343] = 0.75,

[0344] Therefore, according to the provided simulation data and the calculation formula for the combined correction term Δi, the calculation result is 0.75.

[0345] For the said alarm and response, the data collected in real time is compared with a preset threshold to determine whether the data exceeds the limit. If the data exceeds the threshold, the system issues a warning message and feeds back the alarm information to relevant personnel to ensure that problems are processed in a timely manner and the safe and stable operation of the system is guaranteed.

[0346] Although the embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended embodiments and their equivalents.

Claims

1. A fiber optic sensing method based on parameterized waveform buffering and modular coupling, characterized in that: The method includes: Initial data acquisition, presetting a sampling frequency, and collecting building data; External data monitoring, monitoring environmental parameters to obtain environmental parameter data; Data preprocessing, preprocessing the building data and environmental parameter data; Data waveform buffering, performing waveform smoothing on the preprocessed data, establishing a data buffer queue, and during the data waveform buffering process, monitoring the changes in the data in real time; Modular data coupling, integrating the building data and environmental parameter data, conducting in-depth analysis and mining, and reducing data errors and inconsistencies; Abnormal data adjustment, detecting data anomalies, identifying the abnormal values in the data, and deleting, replacing, or correcting the abnormal values; Calibration algorithm selection, based on the characteristics of data changes, monitoring and selecting an appropriate calibration algorithm in real time, effectively coping with data changes, and improving the efficiency and accuracy of calibration; Data calibration, comparing the features with the historical database to find differences and deviations, and calibrating the data to correct the deviations and errors; Alarm and response, comparing the data with a preset threshold to determine whether the valid data exceeds the threshold. If it exceeds the limit, an alarm is issued, and processing and feedback are carried out.

2. The fiber optic sensing method based on parametric waveform buffering and modular coupling according to claim 1, characterized in that: The waveform smoothing process for the preprocessed data is as follows: Among them, y i is the i-th data point after smoothing, w is the weight of the smoothing process, representing the contribution of each original data point to the smoothed data point, and ∑ i j=max(1,i=w+1) x j is the sum of the original data points within the window, and max(1, i = w + 1) ensures that the starting point of the window is not less than 1.

3. The fiber optic sensing method based on parametric waveform buffering and modular coupling according to claim 1, characterized in that: Establishing the data buffer queue; ; Among them, Q is a data buffer queue that stores the most recent k smoothed data points, y n = k + 1, y n = k + 2, …, y n = k + n are the smoothed data points in the queue; During the data waveform buffering process, the real-time monitoring of the data changes is as follows: Among them, Δμ is the mean difference of the data changes monitored in real time, μQ is the mean of the current data buffer queue Q, and μQ prev is the mean of the data buffer queue at the previous moment. k1 is the divisor when calculating the mean to ensure that the mean is the average of all data points in the queue, and ∑ n i=n-k+1 y i is the sum of all smoothed data points in the data buffer queue Q.

4. The fiber optic sensing method based on parametric waveform buffering and modular coupling according to claim 1, characterized in that: The analysis formula for the modular data coupling is as follows: Among them, A represents the final analysis result, which is the result of in-depth analysis and mining after integrating building data and environmental parameter data. f represents an analysis function used to combine the weighted building data, environmental parameter data, and consistency correction factor to obtain the final analysis result. ∑ n i=1 and ∑ m j=1 represent summation operations, which are used to perform weighted summation on building data and environmental parameter data respectively. i and j represent the indices of building data and environmental parameter data respectively, and n and m represent the quantities of building data and environmental parameter data respectively. B i represents the i-th building data item, W i represents the weight of the i-th building data item, which is used to reflect the importance of this data item in the analysis. E j represents the j-th environmental parameter data item, which can be specific environmental parameters such as temperature, humidity, noise, air quality, etc. W j represents the weight of the j-th environmental parameter data item, which is used to reflect the importance of this parameter in the analysis. C represents the consistency correction factor, which is used to correct possible errors and inconsistencies between building data and environmental parameter data.

5. The fiber optic sensing method based on parametric waveform buffering and modular coupling according to claim 1, characterized in that: The adjustment formula for the abnormal data adjustment is as follows: Among them, A i is the adjusted i-th data point, O i is the original i-th data point, F i is the outlier flag, which is 1 if it is an outlier and 0 otherwise, D i is the deletion factor, which is 1 if it decides to delete the outlier and 0 otherwise, R i is the replacement factor, which is 1 if it decides to replace the outlier and 0 otherwise, V is the new value used to replace the outlier, C i is the correction factor, which is 1 if it decides to correct the outlier and 0 otherwise, K is the reference value of the corrected data point, σ is the standard deviation of the data set, ϵ is a very small positive number used to avoid a zero denominator, Z i is the standardized value of the i-th data point, MAD is the median absolute deviation of the data set, which is used to measure the dispersion of the data, α, β, γ, ω are various exponents used to adjust the influence degree of different parts, and λ is the coefficient in the correction term.

6. The fiber optic sensing method based on parametric waveform buffering and modular coupling according to claim 1, characterized in that: The calibration algorithm selection, based on the characteristics of data changes, monitoring and selecting an appropriate calibration algorithm in real time, and the skewness formula is as follows: where n is the sample size of the data, x i is the i-th data point, xˉ is the mean of the data, σ is the standard deviation of the data, and (σx i - xˉ) 3 is the cube of each data point after standardization, which is used to measure the degree of deviation of the data point from the mean; The kurtosis formula is as follows: where n is the sample size of the data, x i is the i-th data point, x̄ is the mean of the data, σ is the standard deviation of the data, [(x i - x̄) / σ] 4 is the fourth power of each standardized data point, used to measure the sharpness of the data point relative to the mean, (n - 2)(n - 3)3(n - 1) 2 is a constant term, used to adjust the kurtosis value so that it can be compared with the kurtosis of the normal distribution (the kurtosis value is 3); If the skewness is close to 0 and the kurtosis is close to 3, the data is close to a normal distribution, and calibration algorithm one is selected. If the skewness or kurtosis deviates significantly from the normal value, the data may have a skewed or leptokurtic phenomenon, and calibration algorithm two is selected. Based on the selected calibration algorithm, the data is calibrated to improve the accuracy and reliability of the data.

7. The fiber optic sensing method based on parametric waveform buffering and modular coupling according to claim 1, characterized in that: The calibration formula for the data calibration is as follows: where d i is the i-th element in the current data set D, and d i ′ is the i-th element in the corrected data set D′, and Δi is the combined correction term for the i-th element; The calculation formula for combining the calibration term Δi is as follows: Among them, α is the weight coefficient of the mean difference, β is the weight coefficient of the variance ratio difference, μD is the mean of the current dataset D, μH is the mean of the historical database H, σ 2 D is the variance of the current dataset D, σ 2 H is the variance of the historical database H, and D and H represent the number of elements in the current dataset and the historical database respectively.

8. The fiber optic sensing method based on parametric waveform buffering and modular coupling according to claim 1, characterized in that: For the alarm and response, the real-time collected data is compared with a preset threshold to determine whether the data exceeds the limit. If the data exceeds the threshold, the system issues a warning message and feeds back the alarm information to relevant personnel to ensure that the problem is handled in a timely manner and to ensure the safe and stable operation of the system.

9. Fiber optic sensing device based on parametric waveform buffering and modular coupling, characterized in that: It includes: Optical fiber sensors, used for initial data acquisition, presetting a sampling frequency, and collecting building data; Environmental monitoring sensors, used for external data monitoring, monitoring environmental parameters to obtain environmental parameter data; A central processing unit, which is used for data preprocessing to preprocess building data and environmental parameter data, and for modular data coupling to integrate building data and environmental parameter data, and for abnormal data adjustment to identify outliers in the data, delete, replace or correct the outliers, and for correction algorithm selection to monitor in real time based on the characteristics of data changes and select an appropriate correction algorithm, and for data correction to compare the characteristics with the historical database to find differences and deviations and correct the data; and for alarm and response to compare the data with a preset threshold to determine whether the valid data exceeds the threshold, and if it exceeds the limit, an alarm is issued and processing and feedback are carried out.

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